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\section{Conclusion}
\label{sec:conclusion}

We develop two models for comparison of performance with respect to AUC on the entire training data set of KDD Cup 1998.
We make a linear SVM model using Fast Large Margin learner and a model based on Naive Bayes learner in RapidMiner in order to classify
examples with TARGET\_B = 1 or TARGET\_B = 0.
We use the same preprocessed data and set of $10$ features for both the models for comparison on common grounds. We use an optimized grid
search technique combined with 10-fold cross validation to obtain the parameter $C$ for linear SVM model. We choose Naive Bayes as it's run time is  
significantly less compared to other classifiers like logistic regression and non linear SVM.
We get AUC values of \optauc{} for linear SVM and \optaucb{} for Naive Bayes model. We observe that the AUC value of Naive Bayes improves
if we add more features to the model and we hypothesize the same will hold true for Linear SVM model as well. However, we are not able to verify
this as we are limited by memory bandwidth.
 

 
